brms VS rstan

[2] https://paul-buerkner.github.io/brms/

Okay so first off, I recommend that you read [this](https://link.springer.com/article/10.3758/s13423-016-1221-4) article about "The Bayesian New Statistics", which highlights estimation rather than hypothesis testing from a Bayesian perspective (see Fig. 1, second row, second column). Instead of a t-test, then, we can *estimate the difference* between two groups/variables. If you want to go deeper than JASP etc, I recommend that you use [brms](https://paul-buerkner.github.io/brms/), or, if you want to go even deeper, [Stan](https://mc-stan.org/) (brms is a front-end to Stan).

I think the brms package (https://github.com/paul-buerkner/brms) or the blavaan package (http://ecmerkle.github.io/blavaan/) have support for SEM. I've never done it myself, so I unfortunately can't give you any direction for that in particular. However, I have used stan in multi-level meta-analysis regression (combining multiple CRISPRa experiments to find determinants of CRISPRa activity, see https://github.com/timydaley/CRISPRa-sgRNA-determinants/blob/master/metaAnalysis/NeuronAndSelfRenewalMetaMixtureRegression.Rmd) and had some success.

Anyway, I found I can include weights into the brm function by using gr(RE, by = var) to deal with the heterogeneous variance and it should automatically assume that each observation within a group is correlated according to the brms reference manual.

This is what actually should work- I must be declaring my variables incorrectly. The issue I'm having is that what you refer to as lin , I tried calling a few things, from b to LinPred (which worked in the link here: brms issue 47). When I've tried doing this, I receive errors that say "The following variables are missing from the dataset....[insert variable used to symbolize linear part of the model)". But I believe you're code is on the right path for what needs to be done- I'll try altering my syntax to be sure it resembles yours let you know if it works.

Unfortunately, I can't just tag it onto to the working linear piece because brms doesn't allow for more than 2 level factor covariates in NL formulas. After much googling, I was able to find these brms github posts: 46 47 where they discuss how a NL component can be added. I've tried the syntax used, but it's still throwing errors. Here is one syntax I tried, going off of the information on those two links (where b1=lambda, b2= kappa)

I have a Colab notebook with an R kernel that I'm using to share with students for remote lessons in statistics. This notebook relies on "rstanarm", which is pretty massive with the number of dependencies - it takes ~50minutes to install into a fresh Colab session with install.packages(). It seems the issue is that many of the dependencies of this package need to be compiled from source, which takes a long time on Linux distributions like Colab.

I ran the R code from here